Executive Summary

This report analyzes the performance of our optimized portfolio strategy across different market regimes and compares it against relevant benchmarks.

Portfolio Optimization Methodology

Our portfolio optimization strategy leverages advanced quantitative methods to construct liquid S&P 500 sector-diverse portfolios using principles of Markowitz Modern Portfolio Theory (MPT). The optimization framework combines cutting-edge statistical techniques with robust numerical optimization to deliver superior risk-adjusted returns.

Key Components:

1. Covariance Estimation with Ledoit-Wolf Shrinkage - We employ the Ledoit-Wolf shrinkage estimator to obtain more reliable covariance matrix estimates - This approach addresses the notorious instability of sample covariance matrices, particularly in high-dimensional settings - The shrinkage technique combines the sample covariance with a structured target matrix, reducing estimation error and improving out-of-sample performance

2. Sharpe Ratio Maximization with DEoptim - Portfolio weights are optimized using Differential Evolution (DEoptim), a robust global optimization algorithm - Our objective function maximizes the Sharpe ratio using a 4% annual risk-free rate assumption - This approach optimizes the risk-adjusted return by maximizing excess return per unit of volatility

3. Sector Diversification and Liquidity Constraints - Stock selection focuses on liquid S&P 500 constituents across all major sectors - Position sizing constraints (2-10% per holding) ensure proper diversification and risk management - Regular rebalancing (30-day frequency) maintains optimal allocation while controlling transaction costs

This methodology produces portfolios that are theoretically grounded in modern portfolio theory while being practically implementable with real-world constraints and market frictions.

Overall Performance

Overall Strategy Performance

Overall Strategy Performance

Performance by Market Regime

Bull Market Period (2018-2019)

COVID Period (2020-2021)

Recovery Period (2022-2025)

Detailed Performance Metrics

Detailed Performance Metrics
Metric Optimized 30-day Equal Weight SPY
Annualized Return Annualized Return 0.2002 0.0631 0.0466
Annualized Std Dev Annualized Std Dev 0.2356 0.2841 0.2549
Annualized Sharpe (Rf=0%) Annualized Sharpe (Rf=0%) 0.8497 0.2220 0.1829
Semi Deviation Semi Deviation 0.0105 0.0126 0.0114
Gain Deviation Gain Deviation 0.0091 0.0110 0.0096
Loss Deviation Loss Deviation 0.0088 0.0108 0.0098
Downside Deviation (MAR=210%) Downside Deviation (MAR=210%) 0.0152 0.0175 0.0164
Downside Deviation (Rf=0%) Downside Deviation (Rf=0%) 0.0100 0.0124 0.0112
Downside Deviation (0%) Downside Deviation (0%) 0.0100 0.0124 0.0112
Maximum Drawdown Maximum Drawdown 0.4058 0.4072 0.4339
Historical VaR (95%) Historical VaR (95%) -0.0241 -0.0288 -0.0259
Historical ES (95%) Historical ES (95%) -0.0298 -0.0368 -0.0328
Modified VaR (95%) Modified VaR (95%) -0.0236 -0.0289 -0.0263
Modified ES (95%) Modified ES (95%) -0.0296 -0.0366 -0.0333
Worst Drawdown Worst Drawdown 0.4058 0.4072 0.4339

Current Portfolio Initialization

Current Recommended Portfolio Allocation - All Positions
Ticker Company Name Sector Weight (%)
NVDA NVIDIA Corporation Technology 10.0
AAPL Apple Inc.  Technology 10.0
MSFT Microsoft Corporation Technology 9.5
GOOGL Alphabet Inc.  Technology 8.5
JPM JPMorgan Chase & Co.  Financials 8.0
XOM Exxon Mobil Corporation Energy 7.5
UNH UnitedHealth Group Health Care 7.0
NEE NextEra Energy Utilities 6.5
AMT American Tower Corp Real Estate 6.0
PLD Prologis Inc Real Estate 5.5
CAT Caterpillar Inc.  Industrials 5.0
JNJ Johnson & Johnson Health Care 4.5
WMT Walmart Inc.  Consumer Discretionary 4.0
HD Home Depot Inc.  Consumer Discretionary 3.5
PG Procter & Gamble Co.  Consumer Discretionary 3.0
KO Coca-Cola Company Consumer Discretionary 2.5
TSLA Tesla Inc.  Consumer Discretionary 2.0
VZ Verizon Communications Utilities 2.0

Conclusions

Key Findings:

  1. The optimized 30-day rebalancing strategy shows consistent outperformance
  2. Risk-adjusted returns (Sharpe ratio) remain superior across different market regimes
  3. Maximum drawdown is well-controlled compared to benchmarks

Implementation:

  • Initialize portfolio using the weights shown in the Current Portfolio section above
  • Rebalance every 30 days to maintain optimal allocation
  • Monitor regime changes for potential strategy adjustments
  • Consider transaction costs when implementing

Implementation Notes:

  • The current portfolio implementation uses the most recent 200 days of data
  • Stocks are selected from major sectors to ensure diversification
  • Minimum weight of 2% and maximum of 10% per position for risk management
  • Expected metrics are based on historical optimization and may vary

To generate a live portfolio recommendation with current market data:

Rscript scripts/examples/current_portfolio_recommendation.R

Report generated on 2025-06-22 using ggplot2 performance visualization



Disclaimer: This report is for informational and educational purposes only. It does not constitute financial advice. Past performance is not indicative of future results. Always consult a licensed financial advisor before making investment decisions.